Triple
T5694792
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Tortorella |
E125511
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
John
John Tortorella is an American professional ice hockey coach best known for his fiery personality and successful NHL coaching career, including a Stanley Cup win with the Tampa Bay Lightning.
|
E545348
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: John | Statement: [John Tortorella, givenName, John]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: John Context triple: [John Tortorella, givenName, John]
-
A.
John
John is the given name of John Arbuthnot Fisher, a prominent British admiral and naval reformer of the late 19th and early 20th centuries.
-
B.
John
John is the given name of the American composer John Luther Adams, known for his works inspired by nature and environmental themes.
-
C.
John
John is the given name of John Adams, the prominent American minimalist and post-minimalist composer known for works like "Nixon in China" and "Short Ride in a Fast Machine."
-
D.
John
John is the given first name of American character actor and comedian Rags Ragland.
-
E.
John
John is the given name of actor John Cho, a Korean American performer known for roles in the "Harold & Kumar" films and the "Star Trek" reboot series.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: John Triple: [John Tortorella, givenName, John]
Generated description
John Tortorella is an American professional ice hockey coach best known for his fiery personality and successful NHL coaching career, including a Stanley Cup win with the Tampa Bay Lightning.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: John Target entity description: John Tortorella is an American professional ice hockey coach best known for his fiery personality and successful NHL coaching career, including a Stanley Cup win with the Tampa Bay Lightning.
-
A.
John
John is the given first name of Johnny Bucyk, a Hall of Fame Canadian ice hockey player best known for his long career with the Boston Bruins.
-
B.
John
John is the first name of Jack Ramsay, the renowned American basketball coach and Hall of Famer.
-
C.
John
John is the first name of J. Michael Luttig, a prominent American conservative jurist and former federal appellate judge.
-
D.
John
John is the first name of former NFL quarterback Joey Harrington, who played primarily for the Detroit Lions in the early 2000s.
-
E.
John
John is the given name of John Madden, the famed American football coach, broadcaster, and namesake of the Madden NFL video game series.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69c0082bb19c8190823a4facd3cba79b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c02409e70081909e47f2bd4a50fa12 |
completed | March 22, 2026, 5:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c07ddd0f248190a796055212284542 |
completed | March 22, 2026, 11:40 p.m. |
| NEDg | Description generation | batch_69c08a536ecc8190a4a0391e28076d44 |
completed | March 23, 2026, 12:33 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c08ad660688190b5d3563ac2a9d0d9 |
completed | March 23, 2026, 12:35 a.m. |
Created at: March 22, 2026, 3:45 p.m.